Impacts of assimilating observations from connected vehicles into a numerical weather prediction model

Siems-Anderson, Amanda; Lee, Jared A.; Brown, Barbara G.; Wiener, Gerry; Linden, Seth

doi:10.1016/j.trip.2020.100253

Cited by 7 publications

(5 citation statements)

References 55 publications

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“…Crowdsourcing may provide new, inexpensive sources of observations (Hintz, Vedel, & Kaas, 2019). For example, private citizen's automatic weather stations (Chapman et al, 2017), surface pressure observations from mobile phones (e.g., Hintz, O'Boyle, et al, 2019) and temperature observations from cars or other vehicles (Bell et al, 2022; Siems‐Anderson et al, 2020), are potentially useful sources of observations for convection‐permitting DA. However, there are complex issues regarding data ownership and privacy, quality control (particularly for moving observing platforms such as mobile phones and cars), and dealing with large data volumes to be resolved before these data will see widespread use in NWP.…”

Section: Outlook and Recommendationsmentioning

confidence: 99%

Progress, challenges, and future steps in data assimilation for convection‐permitting numerical weather prediction: Report on the virtual meeting held on 10 and 12 November 2021

Dance

Bannister

et al. 2022

Atmospheric Science Letters

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In November 2021, the Royal Meteorological Society Data Assimilation (DA) Special Interest Group and the University of Reading hosted a virtual meeting on the topic of DA for convection‐permitting numerical weather prediction. The goal of the meeting was to discuss recent developments and review the challenges including methodological developments and progress in making the best use of observations. The meeting took place over two half days on the 10 and 12 November, and consisted of six talks and a panel discussion. The scientific presentations highlighted some recent work from Europe and the USA on convection‐permitting DA including novel developments in the assimilation of observations such as cloud‐affected satellite radiances in visible channels, ground‐based profiling networks, aircraft data, and radar reflectivity data, as well as methodological advancements in background and observation error covariance modelling and progress in operational systems. The panel discussion focused on key future challenges including the handling of multiscales (synoptic‐, meso‐, and convective‐scales), ensemble design, the specification of background and observation error covariances, and better use of observations. These will be critical issues to address in order to improve short‐range forecasts and nowcasts of hazardous weather.

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Section: Outlook and Recommendationsmentioning

confidence: 99%

Progress, challenges, and future steps in data assimilation for convection‐permitting numerical weather prediction: Report on the virtual meeting held on 10 and 12 November 2021

Dance

Bannister

et al. 2022

Atmospheric Science Letters

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“…The use of crowd‐sourced observations in atmospheric sciences, NWP included, is becoming increasingly widespread. In recent years, several study areas of interest have emerged, such as the use of pressure observations from smartphones (Hintz et al, 2019; Mass & Madaus, 2014; McNicholas & Mass, 2018), the quantification of the urban heat island effect (Meier et al, 2017; Steeneveld et al, 2011), the use of crowd‐sourced wind observations (Droste et al, 2020; Hintz et al, 2020), the use of vehicle observations for assimilation in NWP models (Siems‐Anderson et al, 2020), and the use of PWSs for post‐processing of NWP forecasts (Nipen et al, 2020). Here, we are using crowd‐sourced observations from PWSs in order to post‐process near‐surface temperature forecasts.…”

Section: Datamentioning

confidence: 99%

A transformer neural network for predicting near‐surface temperature

Alerskans

Nyborg

Birk³

et al. 2022

Meteorological Applications

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A new method based on the Transformer model is proposed for post‐processing of numerical weather prediction (NWP) forecasts of 2 m air temperature. The Transformer is a machine learning (ML) model based on self‐attention, which extracts information about which inputs are most important for the prediction. It is trained using time series input from NWP variables and crowd‐sourced 2 m air temperature observations from more than 1000 private weather stations (PWSs). The performance of the new post‐processing model is evaluated using both observational data from PWSs and completely independent observations from the Danish Meteorological Institute (DMI) network of surface synoptic observations (SYNOP) stations. The performance of the Transformer model is compared against the raw NWP forecast, as well as against two benchmark post‐processing models; a linear regression (LR) model and a neural network (NN). The results evaluated using PWS observations show an improvement in the 2 m temperature forecasts with respect to both bias and standard deviation (STD) for all three post‐processing models, with the Transformer model showing the largest improvement. The raw NWP forecast, LR, NN and Transformer model have a bias and STD of 0.34 and 1.96°C, 0.03 and 1.63°C, 0.10 and 1.53°C and 0.02 and 1.13°C, respectively. The corresponding results using DMI SYNOP stations also show improved forecasts, where the Transformer model performs better than both the raw NWP forecast and the two benchmark models. However, a dependence on distance to the coast and cold temperatures is observed.

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“…The use of vehicle-based observations in NWP is still in its infancy, but their use for nowcasting has been investigated by the German weather service (DWD) (Hintz et al, 2019a). Additionally, an observing simulation system experiment conducted by Siems- Anderson et al (2020) showed a modest but appreciable impact from assimilating simulated vehicle-based observations.…”

Section: Introductionmentioning

confidence: 99%

Exploring the characteristics of a vehicle‐based temperature dataset for kilometre‐scale data assimilation

Bell

Dance

Waller

2022

Meteorological Applications

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Crowdsourced vehicle-based observations have the potential to improve forecast skill in convection-permitting numerical weather prediction (NWP). The aim of this paper is to explore the characteristics of vehicle-based observations of air temperature in the context of data assimilation. We describe a novel lowprecision vehicle-based observation dataset obtained from a Met Office proofof-concept trial. In this trial, observations of air temperature were obtained from built-in vehicle air-temperature sensors, broadcast to an application on the participant's smartphone, and uploaded, with relevant metadata, to the Met Office servers. We discuss the instrument and representation uncertainties

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Impacts of assimilating observations from connected vehicles into a numerical weather prediction model

Cited by 7 publications

References 55 publications

Progress, challenges, and future steps in data assimilation for convection‐permitting numerical weather prediction: Report on the virtual meeting held on 10 and 12 November 2021

Progress, challenges, and future steps in data assimilation for convection‐permitting numerical weather prediction: Report on the virtual meeting held on 10 and 12 November 2021

A transformer neural network for predicting near‐surface temperature

Exploring the characteristics of a vehicle‐based temperature dataset for kilometre‐scale data assimilation

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